[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121753-en":3,"doc-seo-121753-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121753,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","Forecasting Workforce Requirement for State Transportation Agencies - A Machine Learning Approach","A decline in available construction engineers and inspectors at State Transportation Agencies (STAs) increases the need for workforce planning. This thesis builds machine learning models to estimate STAs’ person-hour requirements at both agency and project levels. Using Arkansas Department of Transportation (ARDOT) data from 2012–2021, it develops regressors (linear, tree ensembles, kernel-based, neural networks) for project forecasting and time-series plus neural network approaches for monthly agency forecasting. Model comparisons use parametric and non-parametric tests.","University of Arkansas, Fayetteville  \nScholarWorks@UARK  \nGraduate Theses and Dissertations  \n8-2023  \nForecasting Workforce Requirement for State Transportation Agencies: A Machine Learning Approach  \nAdedolapo Mojed Ogungbire University of Arkansas-Fayetteville  \nFollow this and additional works at: [https://scholarworks.uark.edu/etd](https://scholarworks.uark.edu/etd)  \n Part of the Civil Engineering Commons  \nCitation  \nOgungbire, A. M. (2023) . Forecasting Workforce Requirement for State Transportation Agencies: A Machine Learning Approach. Graduate Theses and Dissertations Retrieved from  \n[https://scholarworks.uark.edu/etd/4842](https://scholarworks.uark.edu/etd/4842)  \nThis Thesis is brought to you for free and open access by ScholarWorks@UARK. It has been accepted for inclusion in Graduate Theses and Dissertations by an authorized administrator of ScholarWorks@UARK. For more information, please contact [scholar@uark.edu](scholar@uark.edu).  \nForecasting Workforce Requirement for State Transportation Agencies: A Machine Learning  \nApproach  \nA thesis submitted in partial fulfillment  \nof the requirements for the degree of  \nMaster of Science in Civil Engineering  \nby  \nAdedolapo Ogungbire  \nOsun state University, Nigeria  \nBachelor of Engineering in Civil Engineering, 2018  \nAugust 2023  \nUniversity of Arkansas  \nThis thesis is approved for recommendation to the Graduate Council.  \nSuman Mitra, Ph.D. Thesis Director  \nSarah Hernandez, Ph.D. Committee Member  \nLakeshmi Sasidharan, Ph.D. Committee Member  \nABSTRACT  \nA decline in the number of construction engineers and inspectors available at State Transportation Agencies (STAs) to manage the ever-increasing lane miles has emphasized the importance of workforce planning in this sector. One of the crucial aspects of workforce planning involves forecasting the required workforce for any industry or agency. This thesis developed machine learning models to estimate the person-hour requirements of STAs at the agency and project levels. The Arkansas Department of Transportation (ARDOT) was used as a case study, using its employee data between 2012 and 2021. At the project level, machine learning regressors ranging from linear, tree ensembles, kernel-based, and neural network-based models were developed. At the agency level, a classic time series modeling approach, as well as neural networks-based models, were developed to forecast the monthly person-hour requirements of the agency. Parametric and non-parametric tests were employed in comparing the models across both levels. The results indicated a high performance from the random forest regressor, a tree ensemble with bagging, which recorded an average R-squared value of 0.91. The one-dimensional convolutional neural network model was the most effective model for forecasting the monthly person requirements at the agency level. It recorded an average RMSE of 4,500 person-hours monthly over short-range forecasting and an average of 5,000 personhours monthly over long-range forecasting. These findings underscore the capability of machine learning models to provide more accurate workforce demand forecasts for STAs and the construction industry. This enhanced accuracy in workforce planning will contribute to improved resource allocation and management.  \n© 2023 by Adedolapo Ogungbire All Rights Reserved  \nACKNOWLEDGEMENTS  \nI would like to acknowledge the role and support of everyone who were instrumental to the completion of my master thesis. First, my immense gratitude goes to my advisor, Dr. Suman Mitra for his invaluable advice and continuous support in my academic career. I am deeply grateful to my committee members, Dr. Sarah Hernandez and Dr. Lakeshmi Sasidharan for their roles and unwavering support, invaluable comments and suggestions and a memorable thesis defense. I also want to graciously acknowledge the Arkansas Department of Transportation (ARDOT) for providing access to the data utilized in this study.  \nTo my coll","cbCaivvOAbi4nu7y","https://ap.wps.com/l/cbCaivvOAbi4nu7y","pdf",3311823,1,82,"English","en",105,"# Chapter 1. Introduction\n# Chapter 2. Literature Review\n## 2.1 Qualitative Models\n## 2.2 Quantitative Models\n## 2.2.1 Time Series Models\n## 2.2.2 Regression Models\n## 2.2.3 Analytical stock-and-flow Models\n# Chapter 3. Data and Methodology\n## 3.1 Data\n## 3.1.1 Employee data","[{\"question\":\"Why is workforce planning important for State Transportation Agencies (STAs)?\",\"answer\":\"A declining supply of construction engineers and inspectors makes it harder to manage expanding lane miles, increasing the need to forecast workforce demand.\"},{\"question\":\"What data and case study were used in the thesis?\",\"answer\":\"The thesis uses employee data from the Arkansas Department of Transportation (ARDOT) covering 2012 to 2021.\"},{\"question\":\"Which models performed best for agency and project forecasting?\",\"answer\":\"For agency-level monthly forecasting, the one-dimensional convolutional neural network was most effective. For project-level forecasting, the random forest regressor with bagging showed strong performance (average R-squared about 0.91).\"}]","Forecasting Workforce Requirement for State Transportation Agencies - A Machine Learning Approach | PDF",1785806652,207,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"forecasting-workforce-requirement-for-state-transportation-agencies-a-machine-learning-approach","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/forecasting-workforce-requirement-for-state-transportation-agencies-a-machine-learning-approach/121753/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is workforce planning important for State Transportation Agencies (STAs)?","Question",{"text":75,"@type":76},"A declining supply of construction engineers and inspectors makes it harder to manage expanding lane miles, increasing the need to forecast workforce demand.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data and case study were used in the thesis?",{"text":80,"@type":76},"The thesis uses employee data from the Arkansas Department of Transportation (ARDOT) covering 2012 to 2021.",{"name":82,"@type":73,"acceptedAnswer":83},"Which models performed best for agency and project forecasting?",{"text":84,"@type":76},"For agency-level monthly forecasting, the one-dimensional convolutional neural network was most effective. 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